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Collecting, Analyzing and Visualizing Tweets using Open Source Tools
Full text linkThis tutorial will teach participants how to collect, analyze and visualize results from twitter data. We will demonstrate several different free, open-source web-based tools that participants can use to collect twitter data (e.g., Archivist, 140kit.com, TwapperKeeper), and show them a few different methods, tools or programs they can use to analyze the data in a given collection. Finally, we will show participants visualization tools and programs they can use to present the analyses, such as tag clouds, graphs and other data clustering techniques. As much as possible this will be a hands-on tutorial, so participants can learn by making their own twitter data collection, analysis and visualization as part of the tutorial
Adjusting process count on demand for petascale global optimization⋆
Full text linkThere are many challenges that need to be met before efficient and reliable computation at the
petascale is possible. Many scientific and engineering codes running at the petascale are likely to
be memory intensive, which makes thrashing a serious problem for many petascale applications.
One way to overcome this challenge is to use a dynamic number of processes, so that the total
amount of memory available for the computation can be increased on demand. This paper
describes modifications made to the massively parallel global optimization code pVTdirect in
order to allow for a dynamic number of processes. In particular, the modified version of the
code monitors memory use and spawns new processes if the amount of available memory is
determined to be insufficient. The primary design challenges are discussed, and performance
results are presented and analyzed
Parametric Design Optimization of Uncertain Ordinary Differential Equation Systems
Full text linkThis work presents a novel optimal design framework that treats uncertain dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. The inclusion of uncertainty in design is of paramount practical importance because all real-life systems are affected by it. Designs that ignore uncertainty often lead to poor robustness and suboptimal performance. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The uncertainty statistics are explicitly included in the optimization process. Systems that are nonlinear, have active constraints, or opposing design objectives are shown to benefit from the new framework. Specifically, using a constraint-based multi-objective formulation, the direct treatment of uncertainties during the optimization process is shown to shift, or off-set, the resulting Pareto optimal trade-off curve.
A nonlinear vehicle suspension design problem, subject to parametric uncertainty, illustrates the capability of the new framework to produce an optimal design that accounts for the entire family of systems within the associated probability space
A Framework for the Expansion of Spatial Features Based on Semantic Footprints
Full text linkGeographic feature expansion is a common task in Geographic
Information Systems (GIS). Identifying and integrating
geographic features is a challenging task since many of their
spatial and non-spatial properties are described in different
sources. We tackle this expansion problem by defining semantic
footprints as a measure of similarity among features.
Furthermore, we propose three quantifiers of semantic similarity:
spatial, dimensional, and ontological affinity. We show how these
measures dilute, concentrate, harden, or concede the feature
space, and provide useful insights into the semantic relationships
of the spatial entities. Experiments demonstrate the effectiveness
of our approach in semantically associating the most appropriate
spatial features
FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs
Full text linkFATODE is a FORTRAN library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities.
The paper describes the capabilities, implementation, code organization, and usage of this package.
FATODE implements four families of methods -- explicit Runge-Kutta for nonstiff problems and fully implicit Runge-Kutta, singly diagonally implicit Runge-Kutta, and Rosenbrock for stiff problems.
Each family contains several methods with different orders of accuracy; users can add new methods by simply providing their coefficients.
For each family the forward, adjoint, and tangent linear models are implemented.
General purpose solvers for dense and sparse linear algebra are used; users can easily incorporate problem-tailored linear algebra routines.
The performance of the package is demonstrated on several test problems.
To the best of our knowledge FATODE is the first publicly available general purpose package that offers forward and adjoint sensitivity
analysis capabilities in the context of Runge Kutta methods. A wide range of applications are expected to benefit from its use; examples include parameter estimation,
data assimilation, optimal control, and uncertainty quantification
Between a Rock and a Cell Phone: Social Media Use during Mass Protests in Iran, Tunisia and Egypt
Full text linkIn this paper we examine the use of social media, and especially Twitter, in Iran, Tunisia and Egypt during the mass political demonstrations and protests in June 2009, December 2010 - January 2011, and February 2011, respectively. We compare this usage with methods and findings from other studies on the use of Twitter in emergency situations, such as natural and man-made disasters. We draw on our own experiences and participant-observations as an eyewitness in Iran (first author), and on Twitter data from Iran, Tunisia and Egypt. In these three cases, Twitter filled a unique technology and communication gap at least partially. We summarize suggested directions for future research with a view of placing this work in the larger context of social media use in conditions of crisis and social convergence
CU2CL: A CUDA-to-OpenCL Translator for Multi- and Many-core Architectures
Full text linkThe use of graphics processing units (GPUs) in
high-performance parallel computing continues to become more
prevalent, often as part of a heterogeneous system. For years,
CUDA has been the de facto programming environment for
nearly all general-purpose GPU (GPGPU) applications. In spite
of this, the framework is available only on NVIDIA GPUs,
traditionally requiring reimplementation in other frameworks
in order to utilize additional multi- or many-core devices.
On the other hand, OpenCL provides an open and vendorneutral
programming environment and runtime system. With
implementations available for CPUs, GPUs, and other types of
accelerators, OpenCL therefore holds the promise of a “write
once, run anywhere†ecosystem for heterogeneous computing.
Given the many similarities between CUDA and OpenCL,
manually porting a CUDA application to OpenCL is typically
straightforward, albeit tedious and error-prone. In response
to this issue, we created CU2CL, an automated CUDA-to-
OpenCL source-to-source translator that possesses a novel design
and clever reuse of the Clang compiler framework. Currently,
the CU2CL translator covers the primary constructs found in
CUDA runtime API, and we have successfully translated many
applications from the CUDA SDK and Rodinia benchmark suite.
The performance of our automatically translated applications via
CU2CL is on par with their manually ported countparts
A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. I: Methodology
Full text linkData assimilation obtains improved estimates of the state of a physical system
by combining imperfect model results with sparse and noisy observations of reality.
Not all observations used in data assimilation are equally valuable. The ability to
characterize the usefulness of different data points is important for analyzing the
effectiveness of the assimilation system, for data pruning, and for the design of future
sensor systems.
This paper focuses on the four dimensional variational (4D-Var) data assimilation
framework. Metrics from information theory are used to quantify the contribution
of observations to decreasing the uncertainty with which the system state is known.
We establish an interesting relationship between different information-theoretic metrics
and the variational cost function/gradient under Gaussian linear assumptions.
Based on this insight we derive an ensemble-based computational procedure to estimate
the information content of various observations in the context of 4D-Var. The
approach is illustrated on linear and nonlinear test problems. In the companion paper
[Singh et al.(2011)] the methodology is applied to a global chemical data assimilation
problem
Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation
Full text linkData assimilation obtains improved estimates of the state of a physical system
by combining imperfect model results with sparse and noisy observations of reality.
Not all observations used in data assimilation are equally valuable. The ability to
characterize the usefulness of different data points is important for analyzing the
effectiveness of the assimilation system, for data pruning, and for the design of future
sensor systems.
In the companion paper [Sandu et al.(2011)] we derived an ensemble-based computational
procedure to estimate the information content of various observations in
the context of 4D-Var. Here we apply this methodology to quantify two information
metrics (the signal and degrees of freedom for signal) for satellite observations
used in a global chemical data assimilation problem with the GEOS-Chem chemical
transport model. The assimilation of a subset of data points characterized by the
highest information content, gives analyses that are comparable in quality with the
one obtained using the entire data set
The Green500 List: Escapades to Exascale
Full text linkEnergy efï¬ciency is now a top priority. The ï¬rst
four years of the Green500 have seen the importance of en-
ergy efï¬ciency in supercomputing grow from an afterthought
to the forefront of innovation as we near a point where sys-
tems will be forced to stop drawing more power. Even so,
the landscape of efï¬ciency in supercomputing continues to
shift, with new trends emerging, and unexpected shifts in
previous predictions.
This paper offers an in-depth analysis of the new and
shifting trends in the Green500. In addition, the analysis of-
fers early indications of the track we are taking toward exas-
cale, and what an exascale machine in 2018 is likely to look
like. Lastly, we discuss the new efforts and collaborations
toward designing and establishing better metrics, method-
ologies and workloads for the measurement and analysis of
energy-efï¬cient supercomputing